A Clustered Randomized Controlled Trial of the Positive Prevention PLUS Adolescent Pregnancy Prevention Program Robert G. LaChausse, PhD Objectives. To determine the impact of Positive Prevention PLUS, a school-based adolescent pregnancy prevention program on delaying sexual intercourse, birth control use, and pregnancy. Methods. I randomly assigned a diverse sample of ninth grade students in 21 suburban public high schools in California into treatment (n = 2483) and control (n = 1784) groups that participated in a clustered randomized controlled trial. Between October 2013 and May 2014, participants completed baseline and 6-month follow-up surveys regarding sexual behavior and pregnancy. Participants in the treatment group were offered Positive Prevention PLUS, an 11-lesson adolescent pregnancy prevention program. Results. The program had statistically signiﬁcant impacts on delaying sexual intercourse and increasing the use of birth control. However, I detected no program effect on pregnancy rates at 6-month follow-up. Conclusions. The Positive Prevention PLUS program demonstrated positive impacts on adolescent sexual behavior. This suggests that programs that focus on having students practice risk reduction skills may delay sexual activity and increase birth control use. (Am J Public Health. 2016;106:S91–S96. doi:10.2105/AJPH.2016.303414) See editorials, p. S5–S31.
he adolescent birth rate in the United States continues to decline and has dropped below 24.2 births for every 1000 adolescent females aged 15 to 19 years.1 Although this reﬂects overall progress at achieving lower rates of adolescent pregnancy, progress is uneven. For example, in California, Hispanic adolescents continue to have the highest birth rate, at 34.9 per 1000.2 National data reveal that more than 48% of all students in grades 9 to 12 have had sexual intercourse by the time they graduate, and only 41% of adolescents had used a condom the last time they had sexual intercourse, with Hispanic adolescents reporting slightly lower rates of condom use.3 Because a signiﬁcant number of adolescents engage in sexual risk behaviors, the need for effective adolescent pregnancy prevention program cannot be overstated. Although many adolescent pregnancy prevention programs can increase student knowledge about the consequences of
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becoming pregnant, only a few have demonstrated an impact on students’ behaviors. The US Department of Health and Human Services (HHS) sponsored a systematic review that examined the effectiveness of programs aimed to reduce adolescent pregnancies and the associated sexual risk behaviors. Of the approximately 2000 potentially relevant studies published between 1989 and January 2011, 200 met screening criteria for moderate or high-quality studies. Of these, only 31 provided credible evidence that demonstrated a statistically signiﬁcant positive program impact on at least 1 sexual behavior or reproductive health outcome of interest (sexual activity, contraceptive use, or
pregnancy).4 The review concluded that there is a need for improved research quality and reporting to inform policy initiatives and programming decisions. In 2010, the Ofﬁce of Adolescent Health (OAH) within HHS provided funding to rigorously evaluate new and innovative programs aimed at decreasing adolescent pregnancy and sexual risk behaviors. With funding from OAH, Positive Prevention PLUS, an 11-lesson, school-based adolescent pregnancy prevention program, was developed based on existing literature about effective school-based prevention programs. Many adolescent pregnancy prevention programs assume that youths participate in sexual risk-taking behaviors because they lack the knowledge regarding the consequences of unprotected sexual activity.5,6 The existing literature supports prevention programs that use experiential, interactive activities to emphasize abstinence and risk reduction techniques rather than increasing student knowledge regarding reproductive anatomy or facts regarding the consequences of adolescent pregnancy.5 Past research has suggested that the use of social cognitive theory in pregnancy and sexually transmitted disease (STD) prevention, particularly with adolescents, is far superior to other theoretical approaches or simply increasing student knowledge of reproductive anatomy or the consequences of unprotected sexual intercourse.6 Social cognitive theory posits that behavior change occurs through several constructs, including observational learning, behavioral capability, outcome expectations, and self-efﬁcacy.6,7 For example, the adoption of strategies such as the ability to refuse offers to be
ABOUT THE AUTHOR Robert G. LaChausse is with the Department of Public Health Sciences, California Baptist University, Riverside. Correspondence should be sent to Robert G. LaChausse, California Baptist University, 8432 Magnolia Ave, Riverside, CA 92504 (e-mail: [email protected]
). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link. This article was accepted July 25, 2016. doi: 10.2105/AJPH.2016.303414
sexually active or risk-reduction strategies like birth control use are more likely to occur if youths have learned and practiced these skills, and have developed the conﬁdence to use these skills in their everyday life.7 Positive Prevention PLUS aims to improve adolescents’ communication and negotiation skills to help them communicate assertively, abstain from sexual intercourse, and use birth control effectively. This study describes the methods and results of an external impact evaluation of the Positive Prevention PLUS adolescent pregnancy prevention program for an ethnically diverse sample of high school students in Southern California. As part of a clustered randomized controlled trial, I randomly assigned participating high school sites either to a treatment group that implemented the Positive Prevention PLUS program or a control group. Students completed a self-administered survey at baseline (before program implementation) and a 6-month follow-up survey (postprogram implementation). Speciﬁcally, this study aimed to examine the following research questions: 1. What is the impact of Positive Prevention PLUS relative to a control group on the initiation of sexual activity 6 months after the end of the intervention? 2. What is the impact of Positive Prevention PLUS relative to a control group on having sexual intercourse without using birth control in the past 3 months, 6 months after the end of the intervention? 3. What is the impact of Positive Prevention PLUS relative to a control group on ever having been pregnant, 6 months after the end of the intervention? I hypothesized that students who received Positive Prevention PLUS would be less likely to initiate sexual intercourse 6 months after the end of the intervention than would those in the control group. Furthermore, I expected that the program would decrease the likelihood that students would become pregnant (or get someone pregnant), and decrease the likelihood that adolescents would have sex without using birth control.
PROGRAM Positive Prevention PLUS consists of eleven 45-minute lessons aimed at students in grades 9
through 12 delivered by trained classroom teachers during the normal school day in science, health, or physical education courses. The 11-lesson curriculum includes lessons on the beneﬁts of abstinence, assertive communication, refusal skills, accessing reproductive health services, condom negotiation, and condom use. Students practice communication about abstinence and risk reduction skills through scripted role play and other interactive activities (Appendix A, available as a supplement to the online version of this article at http://www.ajph.org). Teachers from participating public schools were provided training before program implementation from the program developers. First, teachers completed a 2-day training that consisted of an overview of the purpose of the project, adolescent pregnancy statistics (United States and locally), and characteristics of effective adolescent pregnancy prevention programs (theory, pedagogy, ﬁdelity, and so on). The program developers demonstrated each of the 11 lessons. On day 2, teachers were asked to demonstrate how they would teach 1 of the 11 lessons (randomly selected) and received feedback from the program developers. These teachers were assigned by their school district to teach the state-mandated health content. After the initial training, teachers were asked to complete an online teacher training program that consisted of various modules to review the key components of lessons and to observe each lesson being taught by a veteran teacher. Approximately 3 weeks after the initial training and 1 week before implementation, teachers were brought back as a group for a 1-day refresher to emphasize key lessons, lesson activities, and to address any concerns teachers might have regarding the program implementation. During program implementation, the program developers periodically communicated with teachers to provide encouragement and technical assistance. Teachers were given a 3-week period in November 2013 to complete the 11 lessons consecutively. Students in control group classrooms received the standard health, science, or physical education curriculum. Schools and teachers in the control group were asked to refrain from providing any sexuality-related classroom instruction or school-wide adolescent pregnancy or STD prevention– focused activities; however, control group
teachers were allowed to discuss human reproduction if relevant to their curriculum (for example, in a biology course).
METHODS I approached 6 school districts in Southern California to assess each district’s ability to participate in the study. I selected potential school districts based on existing data that indicated high adolescent birth rates and a lack of comprehensive sexuality education or adolescent pregnancy prevention programming. Candidate districts were further reﬁned based on their ability to implement an adolescent pregnancy prevention program, maintain the treatment and control contrast, and provide access to eligible students. Once memoranda of understanding were ﬁnalized, parent or guardian consent was requested. This study was registered at the US National Institutes of Health (ClinicalTrials. gov) #0914915.
Study Design I used a clustered randomized control trial to determine program impacts. I calculated the sample size needed for the study a priori using Optimal Design software using an estimated effect size of 0.20, power of 80%, a type I error rate of 5%, and a interclass correlation among the sites (variance accounted for between sites) of 0.08. This resulted in a sample size of 3402 across 21 school sites, randomly assigned across the 2 conditions (treatment and control), and provided 80% power to detect potential program impacts. After parent or guardian consent was obtained, I randomly assigned school sites into either the treatment or control condition using the select cases (RANDOM) function in SPSS version 22 (IBM, Armonk, NY). Treatment sites (n = 11) agreed to implement the Positive Prevention PLUS program in their ninth grade health (n = 6), physical education (n = 1), or science (n = 4) classes. The 10 control sites agreed to not provide any adolescent pregnancy or STD prevention education within the study period.
Study Sample and Flow The study cluster and participant ﬂow are shown in Figure 1. I approached 22 high
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Assessed for eligibility (k = 22 sites; n = 7042)
Excluded (k = 1) • Did not meet initial inclusion criteria
• Parental consent obtained (k = 4267) Randomized (k = 21; n = 4267)
Allocation School sites allocated to control group (k = 10) Number of consented participants at time of assignment (n = 1784)
School sites allocated to intervention group (k = 11) Number of consented participants at time of assignment (n = 2483) Baseline Clusters lost (k = 0) Completed baseline (n = 2149) Date of data collection: October 2013
Clusters lost (k = 0) Completed baseline (n = 1405) Data of data collection: October 2013
Intervention start date: October 2013 Intervention end date: November 2013 Follow-Up Clusters lost to follow-up (k = 0) Completed 6-mo follow-up (n = 1377) Date of data collection: May 2014 List of reason(s) for noncomplete • Absent (n = 20) • Dropped from site (n = 8) • Refused to participate (n = 0)
Clusters lost to follow-up (k = 0) Completed 6-mo follow-up (n = 113) Date of data collection: May 2014 List of reason(s) for noncomplete • Absent (n = 33) • Dropped from site (n = 1) • Refused to participate (n = 2) Analysis Analyzed (k = 11) n = 2113
Analyzed (k = 10) n = 1377
FIGURE 1—CONSORT Diagram: Positive Prevention PLUS, California, 2013–2014
schools in Southern California to participate in this study. Eligibility to participate included interest in the program, a signed memorandum of understanding, and having required science, health, or physical education courses for ninth grade students. One school site became ineligible because there were no ninth grade science, health, or physical education courses. Parental consent was obtained from the 21 school sites before random assignment; I had distributed the parent or guardian consent forms to the teachers. Teachers distributed the consent forms to students. The consent form described the general purpose of the study, conﬁdentiality, and my contact information. The consent form was provided in both
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English and Spanish. A small incentive (2 movie tickets) was offered to the teacher with the highest percentage of parent consent forms returned at each school site, regardless of whether parents consented or not. After parent or guardian consent was obtained, I randomly assigned the 21 school sites into either the treatment or control condition. School sites, students, and their parents or guardians were not aware of their respective condition before consent. A total of 7042 students were eligible to participate in the study. Of the 4969 students who returned the parent consent form, 4267 had parental consent to participate. Of those students, 3554 students participated in the baseline survey in October 2013, and of those, 3490
students participated in the 6-month followup survey in May 2014. The ﬁnal analytic sample (i.e., those who provided both baseline and 6-month follow-up data) consisted of 2113 participants from 11 sites in the intervention group and 1377 from 10 sites in the control group (Figure 1). Both overall and differential attrition was assessed. The differential attrition rate (i.e., the difference in attrition rates between the treatment and control groups) was approximately 8%, and the overall attrition rate (i.e., the proportion of the sample randomly assigned to the study groups for whom follow-up data were not available) was 18% from randomization to 6-month follow-up. Although the reasoning is largely unknown, attrition from consent to baseline data collection most likely resulted from students being absent the day of baseline data collection or having transferred to another school between the time of consent and the time of data collection.
Data Collection Baseline data were collected for both the treatment and control groups in October 2013. Six-month follow-up data collection occurred in May 2014. Participants completed a self-administered, paper and pencil survey during their regular class period. All survey data were collected by my research assistants and myself. Program developers, teachers, site administrators, and other school staff were not allowed to provide any instructions or guidance, or answer student questions during survey administration. Baseline data collection occurred approximately 8 weeks after parental consent forms were distributed and 1 week before the beginning of the program. For the 6-month follow-up, students were pulled into a central location (e.g., library) by their original class period at baseline. Data were collected on the same date for all study schools (both treatment and control) in each district.
Instrumentation The student survey included a brief demographic characteristics section (gender, age, Hispanic or not Hispanic, and race) and outcome measures, including: (1) whether participants have ever had sexual intercourse, (2) whether participants had ever been pregnant or gotten someone pregnant, and (3)
whether they had sex without birth control in the past 3 months. Respondents were asked whether they were Hispanic (yes or no) and their race (American Indian, Asian, Black or African American, Native Hawaiian or other Paciﬁc Islander, and White.) The average time to complete the survey was 14 minutes, and the reading level was 5.7 (Flesch-Kincaid grade-level equivalent). Skip patterns were used so that if a participant reported that they had never had sex, they were instructed to skip the items pertaining to sexually active respondents and complete the remaining items in the survey.
Study Variables The student survey assessed 3 outcome measures: (1) ever had sexual intercourse, (2) ever been pregnant or gotten someone pregnant, and (3) ever had sexual intercourse without using birth control in the past 3 months. Sexual initiation was constructed from the survey question “Have you ever had sexual intercourse?” A dummy variable was created in which respondents who responded yes were coded as 1 and those who responded no were coded as 0. The item “To the best of your knowledge, have you been pregnant or gotten somebody pregnant?” was used to measure pregnancy. Another dummy variable was created with respondents who respond yes coded as 1 and those who responded no coded as 0. Missing data resulting from the skip pattern of the survey were logically imputed to 0 because it could be inferred that someone who had never had sex had never been pregnant. The outcome variable measuring birth control use was based on the question “In the past 3 months, have you had sexual intercourse without you or your partner using any of these methods of birth control?” Birth control methods listed included condoms, birth control pills, the patch, the ring (NuvaRing, Merck & Co, Whitehouse Station, NJ), implantable uterine device, and implant (Implanon, Merck & Co). A dummy variable was created in which individuals responding yes to having had sex in last 3 months without birth control were coded as 1 and those who responded no coded as 0. Missing data resulting from the skip pattern were coded as 0 because it could be inferred that someone who had never had sex had also
never had sex without birth control (Appendix B, available as a supplement to the online version of this article at http://www. ajph.org).
Statistical Analyses Analyses were based on the intent-to-treat framework, that is, all students were analyzed based on the condition to which they were initially assigned. Because the units of analysis (students) differed from the units of assignment (schools), all inferential analyses used school random effects to adjust SEs for the nonindependence of student observations. I conducted a series of analytic steps to understand the extent to which student observations were nonindependent (correlated) within schools. First, I ﬁt an unconditional, random effects analysis of variance to the data, to estimate the proportion of variance in outcomes attributable to between school differences in outcomes, relative to the total variance in the outcome (between-school and within-school differences). Second, I conducted inferential models that estimated the effect of the intervention, after adjusting for baseline covariates (gender and Hispanic or not). As noted previously, school random effects were incorporated in all inferential analyses, so that the SEs for the treatment variable were appropriately adjusted for the differing unit of treatment
assignment and unit of analysis. I conducted all analyses using the XTMIXED function in Stata version 8.0 (StataCorp, College Station, TX), a procedure that allows this type of random effects modeling. The Benjamini– Hochberg adjustment was made to control for the false discovery rate at 0.05 across the 3 a priori hypotheses.8
RESULTS The analytical sample included all students in the school sites that obtained parental consent and provided data at both baseline and at the 6-month follow-up. Data were pooled across the school sites. Baseline equivalence for the analytical sample on demographic characteristics and outcome measures are provided in Table 1. I calculated equivalence using a linear regression model that predicted the variable of interest from a grouping variable (dummy coded) for each measure, adjusting for the clustering effect using Huber–White adjusted standard errors.9,10 After adjusting for the clustered nature of the data, I observed no statistically signiﬁcant differences at baseline (Table 1). To determine if participants who dropped out of the study in the treatment group differed from those participants who dropped out in the control group, I examined
TABLE 1—Summary Statistics of Key Baseline Measures for the Final Analytic Sample: Positive Prevention PLUS, California, 2013–2014
Baseline Measure Age, y Female gender
Treatment Group, % Control Group, % Treatment vs Control, or Mean 6SD or Mean 6SD Mean Difference (P a) 14.63 60.50
Ever had sexual intercourse
Ever been pregnant or gotten someone pregnant
Ever had sexual intercourse without using birth
control in past 3 mo Sample sizeb a
Estimate with Huber–White Robust SEs. Because of item nonresponse, numbers vary slightly by variable.
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equivalence. I found no signiﬁcant differences for the proportion of females (P = .87), proportion of Hispanics (P = .89), ever had sex (P = .52), ever been pregnant (P = .97), or ever had sexual intercourse without using birth control in the past 3 months (P = .68).
TABLE 2—Postintervention Estimated Effects Using Data From Baseline and Six-Month Follow-up Student Surveys to Address the Primary Research Questions: Positive Prevention PLUS, California, 2013–2014
Treament,a Mean 6SD
Control,a Mean 6SD
Treatment vs Control Mean Difference (P )
Ever had sexual intercourse
Ever been pregnant or gotten someone pregnant
Teachers completed 95% of the lesson activities scheduled to be delivered. Ninety-one percent of the students attended all of the offered lessons. Seventy-three percent of the lessons received a high-quality rating. In terms of diffusion, only 14% of the participants in the control group reported learning about adolescent pregnancy prevention–related topics, whereas treatment group students reported learning 60% of the adolescent pregnancy prevention–related health topics. None of the teachers in the control group reported any school-wide activities related to pregnancy prevention, sexuality, HIV/AIDS, or reproductive health.
Ever had sexual intercourse without using birth
Program Impacts The results of the Positive Prevention PLUS program on participants’ likelihood to engage in sexual intercourse, become pregnant, or have sex without birth control are presented in Table 2. There was a signiﬁcant effect of the Positive Prevention PLUS program on delaying sexual activity. Relative to the control group, participants in the treatment group were approximately 4 percentage points less likely to have had sex at 6-month follow-up (b = –0.04; t = –2.38; P = .01). There was a signiﬁcant effect on ever having sex without birth control in the past 3 months measured at the 6-month followup (b = –0.02; t = –2.61; P = .01). Relative to the control group, participants in the treatment group were approximately 2 percentage points less likely to have had sex without birth control at 6-month follow-up. There was no impact of the Positive Prevention PLUS program on getting pregnant at 6-month follow-up (b = –0.01; t = –1.87; P = .07). Approximately 3% to 6% of the data on the outcome measures was missing. I conducted sensitivity analyses, because if missing data produced systematic differences between the complete cases in the treatment group and
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control in past 3 mo Sample size
Note. Follow-up surveys administered 6 months after the program. a Using estimated adjusted means. Benjamini–Hochberg adjustment was made to control for the false discovery rate.
the complete cases in the control group, then the impact estimates could be biased. To explore the robustness of results from the benchmark sample, I conducted 2 different sensitivity analyses. First, I used multiple imputation to address outcome item nonresponse among the analytic sample. I created 5 multiple imputed data sets, and I computed the regression models used in the impact analysis on the each of the 5 imputed data sets and on the pooled estimates. Variables used in the imputation included the same study outcomes of interest and the covariates (gender, age, and Hispanic [binary]) used in the benchmark analysis. I conducted the imputations separately for treatment and control groups. When a pooled impact was estimated across the multiple imputed data sets, the results were the same direction for all outcomes, but were not statistically signiﬁcant because of the introduction of imprecision in the imputed data. The second sensitivity analysis used logical imputation. That is, imputing a missing score based on how each respondent would have answered because of their other responses on surveys. For example, if a participant had reported having sex in their lifetime at pretest but reported had not having sex at posttest, their score on posttest was changed to having had sex in their lifetime (i.e., carried through to follow-up). When impacts for this version of the outcomes were assessed, the impacts were in the same direction as the benchmark analysis (favoring the treatment arm), but only the result for “ever had sex” remained statistically signiﬁcant. The program impacts were predominantly robust to alternative
speciﬁcations, and sensitivity analyses conﬁrmed the benchmark ﬁndings of statistically signiﬁcant results (Appendix B).
DISCUSSION My study focused on the impact of the Positive Prevention PLUS adolescent pregnancy prevention program on delaying sexual intercourse, preventing pregnancy, and increasing the use of birth control among those students who were offered the program. The results suggested that the program had a statistically signiﬁcant impact on both delaying sexual intercourse and reducing unprotected sexual intercourse 6 months after the end of the program. However, the program did not have a statistically signiﬁcant impact on whether participants would become pregnant (or get someone pregnant). Although these program effects were modest, they are practically meaningful because of the large number of school-aged youths who become sexually active, the consequences of adolescent pregnancy, and the lack of program impacts found in many adolescent pregnancy prevention approaches.4,5 In addition, these ﬁndings are consistent with a previous study of an earlier version of Positive Prevention aimed at decreasing risk behaviors related to HIV/STD infection, although the previous study did not examine pregnancy.11
Study Limitations The use of a clustered randomized control trial with an analytic sample equivalent at baseline provided rigorous evidence
concerning the effectiveness of the program. Although the results of this were promising, several methodological limitations should be noted. First, data were collected using selfreported surveys, and reporting bias could occur in either direction. For example, participants in the treatment group might have been less likely to report being sexually active because they were embarrassed to admit to a behavior the program discourages, and this underreporting could have led to a spurious ﬁnding. Although it was impossible to be completely conﬁdent of the validity of selfreport responses, there was some evidence that supported the general validity of adolescents’ self-report of health behaviors.12 Second, only 14% of the participants were sexually active, making it difﬁcult to examine program impacts on pregnancy rates. This might be a result of the young age (grade 9) of the participants. Third, the overall attrition rate was 18% from randomization to follow-up, and the differential attrition rate (attrition between the treatment and control groups) was 8%. Finally, variation in program implementation might have affected the strength of the treatment received. Prevention programs are seldom implemented perfectly, and several studies have revealed the extent to which program ﬁdelity occurs, how this affects program outcomes, and have replicated evidence-based adolescent pregnancy prevention programs to scale.12 The ﬁndings of the implementation ﬁdelity study have been submitted elsewhere.
Conclusions Because a signiﬁcant number of youths engage in sexual risk behaviors, the need for effective adolescent pregnancy programs cannot be overstated. Although many adolescent pregnancy prevention programs have been shown to increase knowledge, only a few have demonstrated an impact on students’ behaviors.13–15 Findings from this study suggest that the Positive Prevention PLUS program is effective in the short term in delaying sexual initiation and increasing birth control use. However, it is possible that the impact of the program on a reduction in risky sexual behavior could just be an extension of the reduction in sexual initiation and not a further increase in contraceptive use. That is, because risky sexual behavior was
operationalized in such a way that youths were characterized as not engaging in risky sex if they either (1) only had sex while using birth control or (2) abstained from sex, the impact on delaying initiation might have played a role in the observed impact on having sexual intercourse without using birth control. These results have implications for both health educators and researchers. The ﬁndings provided evidence that programs that emphasize risk-reduction skills while limiting biomedical information are effective in reducing sexual risk-taking behavior. Adolescents need to learn and practice risk-reduction skills that they can use in their everyday life. Furthermore, public health researchers should focus future studies on investigating the mechanisms by which adolescent pregnancy prevention programs affect behavior. This could include an examination of the possible relationships among program activities, determinates of behaviors (e.g., selfefﬁcacy, attitudes, behavioral capability), and behavioral outcomes (e.g., birth control use). In addition, the evidence base would be enhanced by exploring the long-term impacts of the Positive Prevention PLUS program on adolescent sexual risk-taking behaviors and adolescent pregnancy. ACKNOWLEDGMENTS This research was supported by grant number TP2AH000007 from the Ofﬁce of Adolescent Health (OAH), Department of Health and Human Services (HHS). I am grateful to Jessica Folmer for her assistance in data collection and data entry, and to the students, teachers, and program staff who participated in the study. Finally, I would like to thank Russell Cole, PhD, at Mathematica Policy Research for his guidance and helpful comments on drafts of this article. Note. The views expressed in this article are those of the author and do not necessarily represent the policies of the HHS or the OAH.
4. Goesling B, Colman S, Trenholm C, Terzian M, Moore K. Programs to reduce teen pregnancy sexually transmitted infections, and associated sexual risk behaviors: a systematic review. J Adolesc Health. 2014;54(5): 499–507. 5. Pedlow CT, Carey MP. Developmentally appropriate sexual risk reduction interventions for adolescents: rationale, review interventions, and recommendations for research and practice. Ann Behav Med. 2004;27(3): 172–184. 6. Rolleri LA, Fuller TR, Firpo-Triplett R, Lesesne CA, Moore C, Leeks KD. Adaptation guidance for evidence-based teen pregnancy and STI/HIV prevention curricula: from development to practice. Am J Sex Educ. 2014;9(2):135–154. 7. Grifﬁn KW, Botvin GJ, Nichols TR. Effects of a school-based drug abuse prevention program for adolescents on HIV risk behaviors in young adulthood. Prev Sci. 2006;7(1):103–112. 8. Raudenbush S, Bryk A. Hierarchical Linear Models. 2nd ed. Thousand Oaks, CA: Sage Publications; 2002. 9. Schochet P. Technical Methods Report: Guidelines for Multiple Testing in Impact Evaluations (NCEE 2008#4018). Washington, DC: US Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance; 2008. 10. Williams RL. A note on robust variance estimation for cluster-correlated data. Biometrics. 2000;56(2):645–646. 11. LaChausse RG. Evaluation of the positive prevention HIV/STD curriculum. Am J Health Educ. 2006;37(4): 203–209. 12. Dusenbury L, Brannigan R, Falco M, Hansen W. A review of research on ﬁdelity of implementation: implications for drug abuse prevention in school settings. Health Educ Res. 2003;18(2):237–256. 13. Suellentrop K. What Works 2011-2012: CurriculumBased Programs That Help Prevent Teen Pregnancy. Washington, DC: National Campaign to Prevent Teen and Unplanned Pregnancy; 2011. 14. Kirby DB, Laris BA, Rolleri LA. Sex and HIV education programs: their impact on sexual behaviors of young people throughout the world. J Adolesc Health. 2007;40:206–217. 15. Basen-Engquist K, Coyle K, Parcel G, Kirby D, Banspach S, Baumler E. School-wide effects of a multicomponent HIV, STD and pregnancy prevention program for high school students. Health Educ Behav. 2001;28: 166–185.
HUMAN PARTICIPANT PROTECTION The study was approved by the institutional review board at California State University, San Bernardino.
REFERENCES 1. Hamilton BE, Martin JA, Osterman MJK, et al. Births: ﬁnal data for 2014. Natl Vital Stat Rep. 2015;64(12): 1–64. 2. California Department of Public Health, Center for Family, Maternal, Child and Adolescent Health Division. Adolescent births in California 2000-2013. Available at: https://www.cdph.ca.gov/data/statistics/ Documents/2013ABRPressRelease.pdf. Accessed January 30, 2016. 3. Kann L, Kinchen S, Shanklin SL, et al. Youth Risk Behavior Surveillance—United States, 2013. MMWR Suppl. 2014;63(4):1–168.
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